• Int J Nurs Stud · Mar 2019

    Controlled Clinical Trial

    Can proactive rapid response team rounding improve surveillance and reduce unplanned escalations in care? A controlled before and after study.

    • Valerie Danesh, Donna Neff, Terry L Jones, Karen Aroian, Lynn Unruh, Diane Andrews, Lotricia Guerrier, Sam J Venus, and Edgar Jimenez.
    • University of Texas at Austin, School of Nursing, 1710 Red River St., Mail Code D0100, Austin, TX 78701, United States. Electronic address: vdanesh@nursing.utexas.edu.
    • Int J Nurs Stud. 2019 Mar 1; 91: 128-133.

    BackgroundUnplanned escalations manifest as a breakdown of hospital care attributable to clinician error through missed or delayed identification of physiological instability, ineffective treatment, or iatrogenic harm.ObjectivesTo examine the impact of an Early Warning Score-based proactive rapid response team model on the frequency of unplanned intra-hospital escalations in care compared with a rapid response team model based on staff nurse identification of vital sign derangements.DesignPre- and post Early Warning Score-guided proactive rapid response team model intervention.Setting237-bed community hospital in the southeastern United States.ParticipantsAll hospitalized adults (n = 12,148) during a pre- and post-intervention period.MethodsLogistic regressions used to examine the relationship between unplanned ICU transfers and rapid response team models (rapid response team vs. Early Warning Score-guided proactive rapid response team).ResultsUnplanned ICU transfers were 1.4 times more likely to occur during the rapid response team baseline period (OR = 1.392, 95% CI [1.017-1.905]) compared with the Early Warning Score-guided proactive rapid response team intervention period.ConclusionsThis study reports a difference in the frequency of unplanned escalations using different rapid response models, with fewer unplanned ICU transfers occurring during the use of Early Warning Score-guided proactive rapid response team model while accounting for differences in admission volumes, age, gender and comorbidities. Implementation of this model has implications for patient outcomes, hospital operations and costs.Copyright © 2019 Elsevier Ltd. All rights reserved.

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